Talent Development as Part of Gart Solutions’ ESG Strategy

In 2026, the line between a thriving digital business and a stagnating one often comes down to how you allocate IT spend. Here's what every tech leader should know — and how Gart Solutions helps you get it right. 64% Average cloud cost reduction via FinOps 81% Compute savings on Azure Spot VMs (AI client) 200× Operational efficiency gains in retail cloud migrations Why this decision matters more than ever Technology spending is no longer a back-office function managed quietly by IT. It's a board-level conversation that shapes cash flow, tax strategy, and competitive agility. The distinction between Capital Expenditure (CapEx) and Operational Expenditure (OpEx) has evolved from a simple accounting rule into a core lever for how fast your organization can move. For most organizations, the shift is already underway — but moving from on-premise infrastructure to cloud isn't just a technical migration. It's a financial transformation that requires the right expertise to execute safely and sustainably. "The question isn't whether to move to OpEx. It's whether you have the discipline and expertise to manage it well — and that's exactly where most teams underestimate the challenge." CapEx vs. OpEx: the fundamentals Before diving into strategy, let's establish clear definitions for how these models apply to IT in practice. Traditional Model Capital Expenditure ▪ Outright purchase of servers, switches, storage hardware ▪ Costs capitalized on the balance sheet (PP&E) ▪ Depreciated over 3–5 years ▪ Large upfront cash outlay in Year 1 ▪ Organization owns & maintains all assets ▪ Rigid capacity planning; expensive to scale Cloud-Native Model Operational Expenditure ✓ Monthly subscriptions to AWS, Azure, GCP ✓ Fully expensed in the current fiscal year ✓ Immediate tax deductibility ✓ Zero upfront capital — pay only for what you use ✓ Provider handles maintenance & upgrades ✓ Elastic scaling: grow or shrink on demand 5-year TCO: the numbers don't lie For a typical mid-market organization (50–150 users), the five-year total cost of ownership tells a compelling story. The on-premise model requires significant capital in Year 1 alone — before a single workload runs in production. Cost component (5-year window)On-premise modelCloud-hosted modelYear 1 capital outlay$58,000 – $128,000$0Hardware refresh / upgradesSignificant periodic reinvestmentIncluded in subscriptionPower, cooling & facilitiesFully borne by the organizationIncluded in service feesIT staff (maintenance focus)High headcount requirementShift to optimization focusTotal estimated TCO (5 years)$553,000 – $1,138,000$350,000 – $820,000 The cloud advantage is real — but only if costs are actively managed. Without a FinOps discipline, cloud bills can grow faster than revenue. That's where Gart Solutions' expertise becomes a strategic asset, not just a vendor relationship. FinOps: the discipline that makes OpEx work Transitioning to OpEx without a financial operations framework is like switching from a fixed salary to freelance income without a budget — the flexibility is there, but the risk of overspending is real. FinOps closes that gap by aligning engineering, finance, and business teams around a shared goal: every cloud dollar tied to measurable business value. 1 Inform — full cost visibility & tagging 2 Optimize — right-size, reserve, spot 3 Operate — governance, automation, policy 64% Average cloud cost optimization delivered by Gart's FinOps engagements. In one AI-driven manufacturing project, Azure Spot VM optimization alone drove an 81% reduction in compute costs — with zero performance impact. How Gart approaches cloud cost optimization Rightsizing & auto-scaling. We analyze actual resource utilization across your entire cloud estate and match instance types to real workload needs — eliminating the most common and expensive mistake in cloud: over-provisioning. AWS Auto Scaling is configured to respond to live demand, not theoretical peaks. Leveraged pricing models. Steady-state workloads move to Reserved Instances or Savings Plans (up to 72% cheaper). Fault-tolerant batch processes and CI/CD agents run on Spot Instances — delivering savings of up to 90% vs on-demand pricing. Storage lifecycle management. We enforce automated tiering policies that move infrequently accessed data from high-performance block storage to cold tiers like Amazon S3 Glacier — significantly reducing one of the fastest-growing cost categories in enterprise cloud. How Gart Solutions accelerates your transition Knowing the theory is one thing. Executing a CapEx-to-OpEx migration safely, at scale, and without disrupting production — that's where engineering expertise matters. Here's what we bring to the table. DevOps & CI/CD automation We integrate automation into every stage of your infrastructure lifecycle — reducing time-to-market and ensuring cloud resources are only provisioned when genuinely needed. Fractional CTO leadership For organizations without senior tech leadership, our Fractional CTO service ensures every infrastructure decision — CapEx or OpEx — is strategically sound and future-proof. Compliance as Code For regulated industries — healthcare (HIPAA, GDPR, NIS2), fintech, and beyond — we embed compliance directly into the infrastructure pipeline, not as an afterthought. Cloud migration & IT audit We begin every engagement with a comprehensive IT audit — identifying hidden costs, legacy dependencies, and optimization opportunities before a single workload moves. Tax advantages of the OpEx model One of the most underappreciated benefits of cloud adoption is its impact on tax strategy. When your organization pays $20,000 in cloud hosting fees, that entire sum is typically deductible in the current fiscal year — providing immediate relief rather than a multi-year depreciation schedule. In the UK, the reformed RDEC scheme now covers cloud computing and data costs as qualifying R&D expenditure (from April 2023). In the US, cloud compute rental expenses can qualify as Research Expenditures under the R&E tax credit. For engineering-led organizations running simulations, model training, or data-intensive R&D workloads, this represents significant annual savings that compound with scale. Software development costs can also be capitalized as intangible assets once the application development stage begins — blending CapEx vs. OpEx treatment strategically to manage margins. A close CTO-CFO partnership is essential to classify engineering activity correctly. Looking ahead: CapEx vs. OpEx As AI and ML workloads become central to product strategy, the cost management challenge intensifies. GPU compute is structurally more expensive than standard instances, and without proactive governance, AI infrastructure costs can spiral rapidly. In this environment, FinOps evolves from an analytical function into an active control mechanism — tracking cost per inference, cost per model version, and optimizing training pipelines continuously. Gart Solutions is already working with AI-driven clients across manufacturing, healthcare, and retail to implement GPU rightsizing, spot instance strategies for training jobs, and automated cost alerting — keeping AI ambitions financially grounded.

The global financial services sector is at an inflection point. For decades, traditional banks have operated on aging mainframe infrastructure that was designed for a batch-processing world — a world without real-time payments, open APIs, or autonomous AI agents. Today, approximately 43% of financial institutions still run core banking systems built more than 20 years ago. These systems, largely written in COBOL and similar procedural languages, have become the single greatest constraint on innovation in banking. This article provides a comprehensive look at core banking modernization — the strategic, technical, and organizational journey from monolithic legacy platforms to composable, cloud-native, AI-ready financial ecosystems. Whether you are a CIO evaluating migration strategies or a product leader planning a fintech digital transformation roadmap, this guide covers everything you need to make informed, high-stakes decisions. The economic burden of legacy infrastructure The most persistent myth in banking IT is that maintaining a legacy system is cheaper than replacing it. In reality, financial institutions consistently underestimate the true total cost of ownership of legacy systems by 70 to 80%. When compliance overhead, integration workarounds, and innovation opportunity cost are factored in, actual IT expenditure runs 3.4 times higher than initially budgeted. 70% Of IT budgets spent maintaining legacy systems rather than building new products 4.7× More spent on compliance for legacy systems vs. modern alternatives $2.41T Cost of poor software quality in the US alone—with $1.52T in accumulated technical debt This is the compounding nature of technical debt in banking legacy system modernization. Every year a bank delays, the workarounds grow more complex, the specialist consultants grow more expensive, and the competitive gap versus digital-native challengers widens. The market has a term for this: the "Innovation Tax." For every dollar spent on IT, less than 30 cents reaches new product development. The core banking modernization market is projected to grow from USD 1.9 billion in 2025 to USD 16.8 billion by 2035, representing a CAGR of 24.4%. This is not a technology trend — it is a survival imperative for traditional financial institutions facing competition from cloud-native challengers. The hidden costs that inflate legacy TCO include: custom coding for minor product updates, exorbitant consulting fees for COBOL specialists, extended QA and testing cycles on unstable codebases, and the maintenance of redundant parallel systems following mergers and acquisitions. Each of these cost drivers compounds the others, creating a system that consumes budget without generating strategic value. The COBOL talent crisis and operational resilience One of the least discussed — yet most urgent — aspects of legacy modernization in fintech is the impending collapse of the specialist workforce that keeps legacy systems running. An estimated 220 billion lines of COBOL code remain in active production globally. These systems process 95% of ATM swipes and the majority of daily financial transactions worldwide. The workforce keeping them alive is rapidly disappearing. The average COBOL programmer is 55 years old. Approximately 10% of this workforce retires every year, with no meaningful pipeline of replacement talent. 60% of organizations using COBOL cite finding skilled developers as their primary operational challenge, leading to delayed security patches and extended system downtime. "Black box" logic — business rules embedded in decades-old code that no current employee fully understands — poses a systemic risk to operational resilience. This is not a distant risk. It is happening now. Banks that have not initiated structured knowledge-capture and code-analysis programs are accumulating operational fragility with every retirement notice. The question is not if your COBOL estate will become unmanageable, but when. Modernization strategies: a practical comparison There is no single correct approach to core banking modernization. The right strategy depends on institutional size, risk appetite, regulatory context, and existing technology investment. Below, we outline the three dominant approaches and their respective trade-offs. Rip and replace — the high-risk path The "Big Bang" or "Rip and Replace" strategy involves decommissioning the legacy system and cutting over to a new platform in a single compressed timeframe. While it promises the fastest path to a clean architecture, its failure rate is significant. If the new system fails at the point of cutover, the bank goes offline entirely — as witnessed in the TSB disaster of 2018, which cost the institution over £600 million in direct and remediation costs. ⚠ Legacy Risk Rip & Replace High-variance, binary outcome: Success is not guaranteed. Catastrophic failure potential: Risk of total system blackout if cutover fails. Massive upfront capital: Requires significant investment before value is realized. Rules lost in translation: Legacy business logic often fails to migrate correctly. Governance complexity: High-stakes Board-level oversight required. Regulatory misalignment: Approval timelines rarely match the aggressive cutover. ✓ Recommended Strategy Phased Modernization Controlled progress: Reversible steps reduce systemic danger. Parallel processing: Legacy systems run alongside new components. Incremental migration: Business logic is preserved and moved slowly. Safe rollbacks: Revert any stage without a full institution outage. Continuous delivery: Unlock new capabilities every few months, not years. Clear audit trails: Easier regulatory engagement via step-by-step validation. Automated refactoring — preserving intellectual property Automated refactoring uses specialized tooling to convert legacy code — typically COBOL — into modern languages such as Java or C#. Critically, this method preserves the unique business logic that has been refined over decades. Rather than rebuilding rules from scratch (and inevitably missing edge cases discovered only after the fact), refactoring transforms the codebase while retaining its institutional knowledge, moving the application to a cloud-native, microservices-compatible foundation. Hollowing out the core — the pragmatic middle path A growing approach among regional and community banks is "hollowing out the core." Rather than replacing the legacy system entirely, this strategy decouples critical real-time services — transaction authorization, balance management, payment processing — and moves them to a modern cloud-native layer. A "Digital Twin" high-performance ledger handles real-time operations, while the legacy core continues performing non-real-time functions such as regulatory reporting and historical statement management. Key benefit: By moving transaction authorization to a Digital Twin, banks can participate in instant payment networks like FedNow and RTP even when the legacy core is offline for nightly batch processing — unlocking real-time capability without a complete replacement project. Composable banking and the BIAN standard The end-state of fintech digital transformation in core banking is not a new monolith — it is a composable architecture. Rather than replacing one rigid system with another, composable banking assembles best-of-breed capability blocks, interconnected via standardized APIs, that can be independently updated, scaled, or replaced without disrupting the whole. The Banking Industry Architecture Network (BIAN) provides the industry-standard framework for this approach, offering a common service landscape and shared vocabulary that dramatically reduces integration complexity. BIAN-aligned architectures offer three compounding benefits: they reveal redundancies in existing systems that drive consolidation savings; they enable a "change-the-bank" rather than just "run-the-bank" mentality; and they provide the clean integration surface required for embedding advanced AI and autonomous agents. BIAN Domain Strategic Outcome Operational Benefit KYC & Onboarding Rationalized onboarding systems Faster customer acquisition Payments Processing Real-time event architecture Reduced settlement latency Credit Decisioning Continuous compliance layer Improved risk scoring Identity Management Trust as a Service model Monetizable API endpoints Data & Analytics Unified data fabric AI-ready data foundation BIAN-aligned APIs are evolving from internal integration tools into externally exposed capability endpoints — the foundation of embedded finance and Banking-as-a-Service business models. Institutions that align to BIAN now are building the commercial infrastructure of the next decade. AI as a transformation catalyst — from generative to agentic The role of artificial intelligence in core banking modernization is dual: it accelerates the transformation process itself, and it defines the target state that transformation is building toward. Both dimensions deserve serious attention. AI accelerating migration CIOs and engineering leaders are increasingly deploying generative AI to analyze legacy codebases, extract embedded business rules, and automate the conversion of procedural COBOL logic into modern architectures. This approach directly addresses the highest-risk phase of any banking legacy system modernization: the knowledge-capture and code-translation work that, done manually, is slow, expensive, and error-prone. AI-driven development reduces manual effort and dramatically improves migration accuracy. Agentic AI in core banking modernization The shift from generative AI to agentic AI represents a fundamental change in how banking operations are conceived. While 2024 and early 2025 were characterized by chatbots and text summarization, 2026 marks the year of autonomous AI agents capable of executing complex multi-step workflows without continuous human oversight. McKinsey estimates that banks leveraging AI at scale can achieve a 30 to 50% acceleration in development timelines and unlock up to $1 trillion in annual value globally. McKinsey & Company Research In the context of core banking, agentic AI systems can autonomously reconcile ledgers, pre-underwrite loans by reading financial statements in real time, detect fraud in instant payment flows, and prioritize relationship management at scale. However, all of these capabilities depend on one prerequisite: a clean, integrated, real-time data foundation. AI strategy is only as good as the underlying data architecture — and this is precisely what legacy modernization enables. AI Capability Level Application in Banking Strategic Impact Traditional ML Fraud pattern detection and transaction monitoring Risk reduction Generative AI Automated code refactoring and legacy logic extraction Modernization speed Agentic AI Autonomous loan underwriting and workflow execution Operational efficiency Multi-agent Mesh Cross-department collaboration and autonomous orchestration Enterprise agility Managing distributed transactions: consistency vs. scalability One of the most technically challenging aspects of legacy modernization in banking is the transition from the atomic consistency of monolithic databases to the distributed nature of microservices. In a legacy mainframe, a transaction either commits everywhere or rolls back everywhere — an elegantly simple guarantee. In a distributed cloud architecture, achieving the same guarantee requires deliberate architectural patterns. Two-Phase Commit (2PC) The 2PC protocol ensures strong, immediate consistency across multiple nodes through a coordinated "prepare" and "commit" sequence. It guarantees that all systems reflect the same state — essential for core ledger operations such as money transfers where immediate consistency is non-negotiable. The trade-off is latency, a single point of failure at the coordinator node, and data locks that can impede throughput under high volume. The Saga Pattern The Saga pattern decomposes a distributed transaction into a sequence of smaller, independent local transactions. Consistency is maintained through eventual consistency and compensating transactions — reversal actions triggered if any step in the sequence fails. Sagas are more scalable and resilient, making them the preferred choice for long-running workflows in cloud-native environments: loan origination, customer onboarding, complex payment routing. Feature Two-Phase Commit (2PC) Saga Pattern Consistency model Strict / immediate Eventual Transaction type Single global transaction Sequence of local transactions Failure handling Global rollback Compensating transactions Latency Higher (coordination overhead) Lower (local execution) Scalability Limited (global locks) High (loosely coupled) Best suited for Core ledger Fund transfers Onboarding Credit Workflows The strategic decision is not either/or. Well-designed modern core banking architectures apply 2PC for the immutable ledger layer where immediate consistency is the gold standard, and Saga patterns for auxiliary services where agility and scale matter more than synchronous guarantees. Regulatory evolution: FiDA, PSD3, and the BaaS reckoning Core banking modernization is not only a technology imperative — it is increasingly a regulatory mandate. The European and US regulatory environments are actively reshaping what modern banking infrastructure must be capable of, at a speed that legacy systems cannot match. FiDA and the open finance transition The EU's Financial Data Access (FiDA) regulation, expected to become law in 2026, marks the formal transition from Open Banking to Open Finance. FiDA expands consent-based data sharing to cover mortgages, loans, investments, insurance, and pensions — not just payment accounts. For established banks, this means building secure, standardized data-sharing infrastructure capable of delivering complex financial histories to third parties in seconds, with granular customer consent controls centralized in a single dashboard. PSD3 and fraud prevention responsibility Parallel to FiDA, PSD3 and the Payment Services Regulation (PSR) harmonize EU payment services and shift liability for financial losses onto payment service providers that fail to implement adequate fraud prevention. This regulatory shift increases pressure on banks to modernize core systems to support real-time transaction monitoring and stronger multi-factor customer authentication — capabilities that most legacy platforms cannot deliver natively. Regulation Primary Focus Timeline FiDA Data access and open finance framework 2026 / 2027 PSD3 / PSR Fraud prevention and payments harmonization 2026 DORA Digital operational resilience — increased audit frequency Active — 2026 Audits EU AI Act Algorithmic transparency and risk-based AI governance 2025 / 2026 The US BaaS regulatory reckoning In the United States, 2024 and 2025 have seen a significant regulatory crackdown on the Sponsor Bank model underpinning Banking-as-a-Service. Federal agencies including the OCC and FDIC have issued consent orders to multiple banks, forcing them to substantially increase oversight of their fintech partners. The lesson is clear: BaaS is not a simple deposit-gathering product but a complex operational model requiring deep investment in integrated governance, third-party risk management, and real-time monitoring. Going into 2026, compliance is a competitive product feature — not just a back-office burden. Case studies: the anatomy of failure and success The disparity between transformations that succeed and those that fail is rarely technical at its root. It is almost always a function of governance, testing rigor, and the willingness to let technical readiness — not predetermined deadlines — drive the cutover decision. ⚠ Cautionary Tale TSB Bank, 2018 The "Big Bang" Migration Failure 5 million customers migrated over a single weekend. 1.9 million customers locked out of accounts. Mortgage accounts vanished; funds appeared in wrong accounts. £613M total cost: Includes migration, remediation, and fines. System went live with over 4,400 open defects. Primary Failure: Timeline driven by business deadline, not technical readiness. ✓ Model Transformation DBS & HSBC The Digital-Native Rebuild Phased strategy: Replaced monolith incrementally with a native cloud core. 50% cost reduction: Lowered cost-to-income ratio for digital customers. AI deployment accelerated from 18 months to under 5 months. Ranked Best Digital Bank globally multiple years running. HSBC AI AML: Detects 2–4× more suspicious activity and reduces false positives by 60% across 1.2B monthly transactions. The DBS and HSBC cases share a common thread: transformation was treated as an organizational capability-building exercise, not a one-time project. Leadership invested in data infrastructure, engineering culture, and modular architecture before attempting to harvest AI-driven outcomes — and the results compounded over years, not quarters. Modern core banking providers: choosing the right platform The provider landscape for core banking modernization has matured considerably over the past five years. The choice of platform is now among the most consequential strategic decisions a bank can make — and the right answer varies significantly by institution size, geography, and ambition. Provider & HQ Target Market Key Differentiation Temenos Switzerland Global Tier 1 & 2 banks Composable banking cloud with deep global functional richness. Mambu Germany Neobanks & fintechs Pure SaaS / composable structure for rapid market entry. Thought Machine UK Tier 1 retail banks Cloud-native / smart contracts for infinite product flexibility. 10x Banking UK Tier 1 & global banks Cloud-native core with AI-driven migration tooling. Finxact US Community & regional US-localized cloud core tailored for American regulatory frameworks. Fiserv / Jack Henry US Community banks & CUs Massive US market share and integrated TCO ecosystem. Tier 1 banks typically prioritize migration complexity management and global regulatory compliance, gravitating toward platforms like Thought Machine or 10x Banking for their ability to handle the transformation of aging mainframes. Community and regional banks focus on total cost of ownership and API flexibility, often choosing Finxact or Jack Henry to ensure open banking compatibility with fintech partners. The platform decision should always follow the architecture decision — not precede it. The future: banking in 2030 and beyond By 2030, the concept of a core banking system will have evolved from a monolithic booking engine into a distributed, orchestrating platform. The most significant shift will not be technical — it will be relational. Future banking infrastructure will move away from user-facing portals toward autonomous protocols that enable secure, verified interaction between AI agents. In this agent-to-agent economy, a customer's personal AI might negotiate mortgage options or dynamically adjust investment allocations directly with a bank's AI system — reducing the need for direct human interaction at the transactional layer while elevating the human role to relationship and exception management. Gartner predicts that by 2030, agentic AI will make at least 15% of daily decisions autonomously, fundamentally flattening organizational structures and redefining the role of middle management in financial services. Post-quantum cryptography (PQC): Banks must begin implementing quantum-resilient encryption now. The threat timeline from quantum decryption is measured in years, not decades, and migration of cryptographic standards at scale requires significant lead time. Digital assets & CBDCs: Bitcoin, stablecoins, and Central Bank Digital Currencies are establishing themselves as permanent features of future balance sheets, requiring new valuation models and direct integration into core banking ledgers. Banking as mentorship: Automation and real-time data analytics will enable banks to monitor customer financial health continuously, offering proactive advice and personalized interventions — evolving from transactional processors to valued financial partners. By 2028, outdated banking systems are projected to cost global banks over $57 billion annually. The institutions that begin their core banking modernization journey now — systematically, with a clear data and architecture strategy — will define the competitive landscape of the next decade. Those that wait will be paying for the privilege of being disrupted. Ready to launch your core banking modernization? Gart Solutions is a technology-first engineering partner specializing in cloud infrastructure, data platform architecture, and DevSecOps for financial institutions navigating legacy modernization. Cloud-native migration Phased transition from mainframe and on-premise cores to AWS, GCP, or Azure — with zero-downtime architecture and full disaster recovery. Data platform & AI readiness Build the real-time data foundation that legacy systems can't provide — streaming pipelines, clean data lakes, and AI-ready feature stores. API & composable banking Design and implement BIAN-aligned, MACH-principle API layers that unlock open banking, BaaS, and embedded finance partnerships. DevSecOps & compliance Continuous compliance pipelines aligned to DORA, PSD3, and FiDA — shifting security and audit left into the development lifecycle. Move faster — without the Big Bang risk. Start the conversation Key takeaways: core banking modernization Avoid Big Bang: Adopt incremental, sidecar, or "hollow out the core" strategies. The risk profile of full cutover migrations is not acceptable for most institutions. Data first: AI strategy is only as good as the data foundation. Prioritize clean, real-time, integrated data architecture before launching AI initiatives. Embrace BIAN and MACH principles: Standardized, composable architecture avoids vendor lock-in and creates the integration surface for future capabilities. Treat compliance as product: Particularly in BaaS and open finance contexts, regulatory readiness is a competitive differentiator, not just a cost center. Start the COBOL clock: Knowledge-capture and codebase analysis should begin now, before the specialist workforce shrinks further and institutional knowledge is lost.